River water quality analysis and treatment using soft computing technique: A survey

Abstract

The scarcity of potable water increasing day by day. Rivers are the main source of land water for industry and agricultural activity. Unexpected transforms in river low reasoned by imminent tremendous events may enforce grave dilapidation on river water quality and significant impacts on ecosystems. The aim of these lessons is to determine the quality category of water in river and to help extract the key features and problems with the existing systems for water treatment. A number of quantitative models based on system, neural network, fuzzy logic, genetic algorithms, integrated models. Hybrid and many other feature selection techniques are being used in water analysis and treatment by decision-makers in WWTP (waste water treatment plants). It includes twenty five water quality parameters. Water quality is categorized into five levels based on the values of water quality index as Poor (WQI= 0 to 44, Marginal (WQI = 45 to 64), Fair (WQI = 65 to 79), Good (WQI = 80 to 94), and Excellent (WQI= 95 to100). The sensitivity The comparison of the various systems is done on the basis of datasets used for filtration the methodology applied and the platform on which the system is implemented. Thus this report reviews the various expert systems from 1995 to 2015 used for river water quality analysis and treatment.